




"""simple docstring"""




from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowerCAmelCase				:     Union[str, Any]					=				logging.get_logger(__name__)






@add_end_docstrings(UpperCAmelCase__							)
class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				def __init__(			self						,					**_a					):





							"""simple docstring"""
							super().__init__(**_a					)
							requires_backends(self						,					"""vision"""					)
							requires_backends(self						,					"""torch"""					)

							if self.framework != "pt":
										raise ValueError(f'The {self.__class__} is only available in PyTorch.'					)

							self.check_model_type(_a					)





				def   _lowerCAmelCase							(			self						,					**_a					):





							"""simple docstring"""
							lowerCamelCase      =   {}
							lowerCamelCase      =   {}
							lowerCamelCase      =   {}
							# preprocess args
							if "points_per_batch" in kwargs:
										lowerCamelCase      =   kwargs["""points_per_batch"""]
							if "points_per_crop" in kwargs:
										lowerCamelCase      =   kwargs["""points_per_crop"""]
							if "crops_n_layers" in kwargs:
										lowerCamelCase      =   kwargs["""crops_n_layers"""]
							if "crop_overlap_ratio" in kwargs:
										lowerCamelCase      =   kwargs["""crop_overlap_ratio"""]
							if "crop_n_points_downscale_factor" in kwargs:
										lowerCamelCase      =   kwargs["""crop_n_points_downscale_factor"""]
							# postprocess args
							if "pred_iou_thresh" in kwargs:
										lowerCamelCase      =   kwargs["""pred_iou_thresh"""]
							if "stability_score_offset" in kwargs:
										lowerCamelCase      =   kwargs["""stability_score_offset"""]
							if "mask_threshold" in kwargs:
										lowerCamelCase      =   kwargs["""mask_threshold"""]
							if "stability_score_thresh" in kwargs:
										lowerCamelCase      =   kwargs["""stability_score_thresh"""]
							if "crops_nms_thresh" in kwargs:
										lowerCamelCase      =   kwargs["""crops_nms_thresh"""]
							if "output_rle_mask" in kwargs:
										lowerCamelCase      =   kwargs["""output_rle_mask"""]
							if "output_bboxes_mask" in kwargs:
										lowerCamelCase      =   kwargs["""output_bboxes_mask"""]
							return preprocess_kwargs, forward_params, postprocess_kwargs





				def __call__(			self						,					_a						,					*_a						,					_a=None						,					_a=None						,					**_a					):





							"""simple docstring"""
							return super().__call__(_a						,					*_a						,					num_workers=_a						,					batch_size=_a						,					**_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a=64						,					_a = 0						,					_a = 512 / 1_500						,					_a = 32						,					_a = 1						,					):





							"""simple docstring"""
							lowerCamelCase      =   load_image(_a					)
							lowerCamelCase      =   self.image_processor.size["""longest_edge"""]
							lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   self.image_processor.generate_crop_boxes(
							    _a						,					_a						,					_a						,					_a						,					_a						,					_a					)
							lowerCamelCase      =   self.image_processor(images=_a						,					return_tensors="""pt"""					)

							with self.device_placement():
										if self.framework == "pt":
													lowerCamelCase      =   self.get_inference_context()
													with inference_context():
																lowerCamelCase      =   self._ensure_tensor_on_device(_a						,					device=self.device					)
																lowerCamelCase      =   self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""					)					)
																lowerCamelCase      =   image_embeddings

							lowerCamelCase      =   grid_points.shape[1]
							lowerCamelCase      =   points_per_batch if points_per_batch is not None else n_points

							if points_per_batch <= 0:
										raise ValueError(
										    """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
										    """To return all points at once, set points_per_batch to None"""					)

							for i in range(0						,					_a						,					_a					):
										lowerCamelCase      =   grid_points[:, i : i + points_per_batch, :, :]
										lowerCamelCase      =   input_labels[:, i : i + points_per_batch]
										lowerCamelCase      =   i == n_points - points_per_batch
										yield {
										    "input_points": batched_points,
										    "input_labels": labels,
										    "input_boxes": crop_boxes,
										    "is_last": is_last,
										    **model_inputs,
										}





				def   _lowerCAmelCase							(			self						,					_a						,					_a=0.88						,					_a=0.95						,					_a=0						,					_a=1						,					):





							"""simple docstring"""
							lowerCamelCase      =   model_inputs.pop("""input_boxes"""					)
							lowerCamelCase      =   model_inputs.pop("""is_last"""					)
							lowerCamelCase      =   model_inputs.pop("""original_sizes"""					).tolist()
							lowerCamelCase      =   model_inputs.pop("""reshaped_input_sizes"""					).tolist()

							lowerCamelCase      =   self.model(**_a					)

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							lowerCamelCase      =   model_outputs["""pred_masks"""]
							lowerCamelCase      =   self.image_processor.post_process_masks(
							    _a						,					_a						,					_a						,					_a						,					binarize=_a					)
							lowerCamelCase      =   model_outputs["""iou_scores"""]
							lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   self.image_processor.filter_masks(
							    masks[0]						,					iou_scores[0]						,					original_sizes[0]						,					input_boxes[0]						,					_a						,					_a						,					_a						,					_a						,					)
							return {
							    "masks": masks,
							    "is_last": is_last,
							    "boxes": boxes,
							    "iou_scores": iou_scores,
							}





				def   _lowerCAmelCase							(			self						,					_a						,					_a=False						,					_a=False						,					_a=0.7						,					):





							"""simple docstring"""
							lowerCamelCase      =   []
							lowerCamelCase      =   []
							lowerCamelCase      =   []
							for model_output in model_outputs:
										all_scores.append(model_output.pop("""iou_scores"""					)					)
										all_masks.extend(model_output.pop("""masks"""					)					)
										all_boxes.append(model_output.pop("""boxes"""					)					)

							lowerCamelCase      =   torch.cat(_a					)
							lowerCamelCase      =   torch.cat(_a					)
							lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   self.image_processor.post_process_for_mask_generation(
							    _a						,					_a						,					_a						,					_a					)

							lowerCamelCase      =   defaultdict(_a					)
							for output in model_outputs:
										for k, v in output.items():
													extra[k].append(_a					)

							lowerCamelCase      =   {}
							if output_rle_mask:
										lowerCamelCase      =   rle_mask

							if output_bboxes_mask:
										lowerCamelCase      =   bounding_boxes

							return {"masks": output_masks, "scores": iou_scores, **optional, **extra}









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase				:     Optional[int]					=				{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase				:     int					=				[
					    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SwinForImageClassification""",
					    """SwinForMaskedImageModeling""",
					    """SwinModel""",
					    """SwinPreTrainedModel""",
					    """SwinBackbone""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase				:     str					=				[
					    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFSwinForImageClassification""",
					    """TFSwinForMaskedImageModeling""",
					    """TFSwinModel""",
					    """TFSwinPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swin import (
										    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinBackbone,
										    SwinForImageClassification,
										    SwinForMaskedImageModeling,
										    SwinModel,
										    SwinPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_swin import (
										    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSwinForImageClassification,
										    TFSwinForMaskedImageModeling,
										    TFSwinModel,
										    TFSwinPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase				:     int					=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



